Deep learning has significantly transformed face recognition, enabling the deployment of
large-scale, state-of-the-art solutions worldwide. However, the widespread adoption of deep …

Sponge examples are test-time inputs optimized to increase energy consumption and
prediction latency of deep networks deployed on hardware accelerators. By increasing the …

The increasing popularity of deep learning (DL) models and the advantages of computing,
including low latency and bandwidth savings on smartphones, have led to the emergence of …

Sponge attacks aim to increase the energy consumption and computation time of neural
networks deployed on hardware accelerators. Existing sponge attacks can be performed …

The motivation for the development of multi-exit networks (MENs) lies in the desire to
minimize the delay and energy consumption associated with the inference phase. Moreover …

The rise of deep learning (DL) has increased computing complexity and energy use,
prompting the adoption of application specific integrated circuits (ASICs) for energy-efficient …

Abstract Deep Neural Networks (DNNs) have demonstrated remarkable success in various
domains but remain susceptible to adversarial examples: slightly altered inputs designed to …

The past decade presents a massive adoption of machine learning in divers domains. This
fact has been greatly facilitated by cloud computing, which has made high-performance …